SuboptimumG Tutorial#
This notebook will showcase how to use the key features of SuboptimumG. Note that if you ever modify the source code of the package. You have to restart the kernel so that your changes are detected! (Hit the “Restart” button on VSCode). Alternatively, you can also try to uncomment the cell below and run it.
[1]:
# # Make sure to run this cell first
# import subprocess
# import sys
# def run_pip_command(cmd, show_lines=2, show_start=False):
# """Run pip command and show limited output lines"""
# result = subprocess.run(cmd, capture_output=True, text=True)
# output_lines = result.stdout.strip().split("\n")
# if show_start:
# output = "\n".join(
# output_lines[:show_lines] + ["..."] + output_lines[-show_lines:]
# )
# else:
# output = "\n".join(output_lines[-show_lines:])
# print(output)
# # Install requirements (showing last 2 lines)
# run_pip_command([sys.executable, "-m", "pip", "install", "-r", "../requirements.txt"])
# # Install package in editable mode (showing first 2 and last 2 lines)
# run_pip_command([sys.executable, "-m", "pip", "install", "-e", ".."], show_start=True)
Simulating a Single Competition#
We start by simulating the results of a single competition with the default car (REV 9). We load in competition-wide parameters (tracks, competitor statistics, simulation hyperparameters, etc.) from competition.yaml. We load in car-specific parameters from car.yaml. Check these yaml files to see what SuboptimumG currently takes into account and to edit the defaults.
Observe that there are soft plausibility warnings when a parameter is outside of an expected range.
[ ]:
import suboptimumg
import yaml
# Update sheets.yaml with the latest values on Google Drive.
suboptimumg.sheet_to_yaml.grab_sheet_data()
with open("../parameters/sheet.yaml", "r") as file:
sheets_config = yaml.safe_load(file)
# Create sample competition
with open("../parameters/competition.yaml", "r") as file:
comp_config = yaml.safe_load(file)
# NOTE: Those numbers are less verified, could cause unexpected crashes!
# with open("../parameters/car.yaml", "r") as file:
# car_config = yaml.safe_load(file)
# This is based off the sheet.yaml file
michigan2023_revX = suboptimumg.compsim.Competition.from_config(
comp_config=comp_config, car_config=sheets_config
)
We run the competition by calling .run() on the competition object. Then, we can print out our point totals and graph some more detailed information, in this case power v.s. time during the Accel event.
[2]:
import numpy as np
from suboptimumg.plotting.utils import pretty_print_results
from suboptimumg.plotting.generic_plot import plot2D
# Run sample competition
accel_results, skidpad_results, autoX_results, endurance_results, efficiency_points = (
michigan2023_revX.run()
)
pretty_print_results(
accel_results, skidpad_results, autoX_results, endurance_results, efficiency_points
)
plot2D(
x_list=np.array(endurance_results.time_list),
y_list=np.array(endurance_results.power_list) / 1000,
title="Power Usage over a Single Endurance Lap",
x_axis="Time since Lap Start (s)",
y_axis="Power (kW)",
theme="powertrain",
)
print("Power Statistics:")
print(f"Max Power: {max(endurance_results.power_list) / 1000} kW")
print(f"Min Power: {min(endurance_results.power_list) / 1000} kW")
print(
f"Avg Power: {sum(endurance_results.power_list) / len(endurance_results.power_list) / 1000} kW"
)